Current process
Minnesota facilities
Max criteria above 250 tons
fac <- mutate(fac,
max_value = max_crit,
max_crit = ifelse(max_crit > 249.9, 265, max_crit))
fac <- mutate(fac, max_crit = ifelse(max_crit < 5, 5, max_crit))
fac_crit <- group_by(fac, SOURCE_ID) %>%
summarize(max_crit = max_crit[1]) %>%
arrange(max_crit) %>%
mutate(id = 1:n())
#print(nrow(filter(fac_crit, max_crit > 249.9)))
fac_crit$colors <- NA
for(i in 1:nrow(fac_crit)) fac_crit$colors[i] <- (fac_crit$max_crit[i] > 249.9) * runif(1) > 0.6
ggplot(fac_crit, aes(id, max_crit)) +
geom_bar(stat="identity", aes(fill = max_crit > 249.9)) +
scale_fill_manual(values = ton_colors,
name = "",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(fill = guide_legend(ncol = 2, keywidth=3)) +
theme(legend.position="top")
*120 facilities out of 2,000
ggplot(fac_crit, aes(id, max_crit)) +
geom_bar(stat="identity", aes(fill = colors)) +
scale_fill_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(fill = guide_legend(ncol = 2, keywidth=3)) +
theme(legend.position="top")
Max criteria vs. total HAPs
fac <- group_by(fac, SOURCE_ID, Facility) %>%
summarize(max_crit = max_crit[1],
max_value = max_value[1],
total_HAPS = sum(Emissions, na.rm=T),
Formaldehyde = sum(Emissions[Pollutant == "FORMALDEHYDE"], na.rm=T),
Acrolein = sum(Emissions[Pollutant == "ACROLEIN"], na.rm=T),
Lead = sum(Emissions[Pollutant == "LEAD"], na.rm=T),
Nickel = sum(Emissions[Pollutant == "NICKEL"], na.rm=T),
Arsenic = sum(Emissions[Pollutant == "ARSENIC"], na.rm=T),
Metals = sum(Emissions[Pollutant %in% c("LEAD", "NICKEL", "ARSENIC")], na.rm=T))
fac <- filter(fac, max_value > 1, total_HAPS > 0.05) %>%
ungroup() %>%
arrange(max_value)
fac$id <- 1:nrow(fac)
fac$colors <- as.factor(fac$max_value > 249.9)
# HAPs arranged in order of increasing Criteria
ggplot(fac, aes(id, log(total_HAPS))) + geom_point(size=2.8 * size_X, alpha=0.65) + labs(title = "Facility HAPs emissions in order of increasing criteria emissions")
# Total HAPs
p <- ggplot(fac) +
labs(title = "Total HAPs") +
geom_point(data = fac, aes(id, log10(total_HAPS), color = colors), size = 2.5 * size_X, alpha = 0.7) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color= guide_legend(ncol = 2, keywidth=3)) +
theme(legend.position="top")
ggplot(fac) +
labs(title = "HAPs emissions vs maximum Criteria emissions") +
geom_point(data = fac, aes(log10(max_value), log10(total_HAPS), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(total_HAPS)), span = 155) + #method="lm")
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% HAPS emitters
ggplot(filter(fac, total_HAPS > quantile(total_HAPS, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, total_HAPS > quantile(total_HAPS, 0.9)[[1]]), aes(log10(max_value), log10(total_HAPS), color = colors), size = 4 * size_X, alpha = 0.7) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Formaldehyde
ggplot(filter(fac, Formaldehyde > 0.00001)) +
labs(title = "Formaldehyde emissions vs maximum Criteria emissions") +
geom_point(data = filter(fac, Formaldehyde > 0.00001), aes(log10(max_value), log10(Formaldehyde), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(Formaldehyde)), span = 15) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% formaldehyde emitters
ggplot(filter(fac, Formaldehyde > quantile(Formaldehyde, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, Formaldehyde > quantile(Formaldehyde, 0.9)[[1]]), aes(log10(max_value), log10(Formaldehyde), color = colors), size = 4 * size_X, alpha = 0.7) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Acrolein
ggplot(filter(fac, Acrolein > 1E-5)) +
labs(title = "Acrolein emissions vs maximum Criteria emissions") +
geom_point(data = filter(fac, Acrolein > 1E-5),
aes(log10(max_value), log10(Acrolein), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(Acrolein)), span = 15) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% acrolein emitters
ggplot(filter(fac, Acrolein > quantile(Acrolein, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, Acrolein > quantile(Acrolein, 0.9)[[1]]), aes(log10(max_value), log10(Acrolein), color = colors), size = 4 *size_X, alpha = 0.7) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Lead
ggplot(filter(fac, Lead > 1E-5)) +
labs(title = "Lead emissions vs maximum Criteria emissions") +
geom_point(data = filter(fac, Lead> 1E-5),
aes(log10(max_value), log10(Lead), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(Lead)), span = 15) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% lead emitters
ggplot(filter(fac, Lead > quantile(Lead, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, Lead > quantile(Lead, 0.9)[[1]]), aes(log10(max_value), log10(Lead), color = colors), size = 4 * size_X, alpha = 0.7) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Nickel
ggplot(filter(fac, Nickel > 1E-5)) +
labs(title = "Nickel emissions vs maximum Criteria emissions") +
geom_point(data = filter(fac, Nickel> 1E-5),
aes(log10(max_value), log10(Nickel), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(Nickel)), span = 15) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% nickel emitters
ggplot(filter(fac, Nickel > quantile(Nickel, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, Nickel > quantile(Nickel, 0.9)[[1]]), aes(log10(max_value), log10(Nickel), color = colors), size = 4.5 * size_X, alpha = 0.7) +
scale_color_manual(values= c(viridis(10)[8], magma(5)[4]),
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top") 
Arsenic
ggplot(filter(fac, Arsenic > 1E-5)) +
labs(title = "Arsenic emissions vs maximum Criteria emissions") +
geom_point(data = filter(fac, Arsenic > 1E-5),
aes(log10(max_value), log10(Arsenic), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(Arsenic)), span = 15) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% arsenic emitters
ggplot(filter(fac, Arsenic > quantile(Arsenic, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, Arsenic > quantile(Arsenic, 0.9)[[1]]), aes(log10(max_value), log10(Arsenic), color = colors), size = 4 * size_X, alpha = 0.7) +
scale_color_manual(values = ton_colors,
name = "",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Metals
ggplot(filter(fac, Metals > 1E-4)) +
labs(title = "Metals emissions vs maximum Criteria emissions") +
geom_point(data = filter(fac, Metals > 1E-4), aes(log10(max_value), log10(Metals), color = colors), size = 3.5 * size_X, alpha = 0.7) +
geom_smooth(aes(log10(max_value), log10(Metals)), span = 15) +
scale_color_manual(values= ton_colors,
name="",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Top 10% Metal emitters
ggplot(filter(fac, Metals > quantile(Metals, 0.9)[[1]])) +
labs(title = "") +
geom_point(data = filter(fac, Metals > quantile(Metals, 0.9)[[1]]), aes(log10(max_value), log10(Metals), color = colors), size = 4 * size_X, alpha = 0.7) +
scale_color_manual(values = ton_colors,
name = "",
labels = c(" Below 250 tons ", " Above 250 ")) +
guides(color = guide_legend(ncol = 2, keywidth=3, keyheight = 3, override.aes = list(size=8))) +
theme(legend.position="top")
Table
library(knitr)
options(digits = 2)
fac[ ,c(4:11)] <- round(fac[ ,c(4:11)], 3)
DT::datatable(fac[ ,-c(3,12:13)], options=list(searching=F, paging=T, scrollX=T), rownames = FALSE)#kable(fac[ ,-c(3,12:13)])